LiDAR and rice agriculture: Flood modelling and farmer participation for adaptation

Transcription

1 LiDAR and rice agriculture: Flood modelling and farmer participation for adaptation John Colin Yokingco, Luigi Toda, Enrico Paringit, Rodel Lasco 12 May 2016 Adaptation Futures 2016 Goudriaan Room II, Postillion Convention Centre WTC Rotterdam, The Netherlands WHAT? 1

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3 Although planted in flooded fields, prolonged flooding at a certain depth can kill rice plants (Ram et. Al 2002). Background Flooding is one of the major impacts of extreme weather events which can cause damage to property and loss of life. Apalit, Pampanga, one of the many rice producing municipalities of Pampanga has been suffering from prolonged inundation which has led to losses to rice production. 3

4 Objectives Showcase a method that combines flood modelling techniques and participatory methods in generating tools to aid in adaptation to flooding in rice agriculture. Emphasize the importance and advantages of integrating local knowledge and experiences with scientific methods and techniques. Develop a Zonation Classification for rice agriculture flood adaptation. WHERE? 4

5 Study Site 12 Barangays 1 st Class Municipality Gateway to Pampanga from Manila Hydrography The Pampanga River runs through the municipality and is a source for the irrigation of some of its rice fields. The terrain is relatively flat which makes it suitable for agriculture, however it also makes the municipality prone to flooding. 5

6 4.70% 0.15% 12.40% 1.31% 12.25% 63.29% 5.90% 11 of the 12 barangays are riceproducing Approximately 63 % of the municipality is cultivated land grown mostly with perennial crops 6

7 HOW? Methodology VALIDATE OBTAIN CALIBRATE RUN CREATE CONSULT RECOMMEND Current flood models and maps with farmers Current agricultural practices and how they are adapted to floods Flood modelling input data Flood models based on inputs from farmer FGD LISFLOOD-FP to produce models Zone classification maps Experts from the International Rice Research Institute (IRRI) Strategies based on results from FGD, Zone classification maps, and KII 7

8 Light Detection and Ranging (LiDAR) LiDAR is a remote sensing technology that uses rapid laser pulses to map out the surface of the earth. LiDAR data can be used to create high resolution digital surface, terrain, and elevation models which have been used for various applications such as hydrologic modelling. 8

9 Focus Group Discussion Obtained: current agricultural practices such as varieties used flood experiences flood depth, extent, and duration Results FOCUS GROUP DISCUSSION 9

10 NOAH FLOOD MAP 10

11 Rice varieties in Apalit Name of variety NSIC RC 216 (Tubigan 17) PSB Rc10 (Pagsanjan) NSIC Rc222 (Tubigan 18) Hybrid (SL-8H) NSIC Rc290 IR 64 NSIC Rc150 Type of variety, suitable environment Irrigated Droughttolerant, irrigated Irrigated Irrigated Salinetolerant Submergence -tolerant Irrigated Cost (Php/kg) Days to maturity Yield (cav/ha) /40 kg /40 kg / kg 4800/15 kg Plant height (cm) / kg ,265/40 kg * Results FLOOD MODELS 11

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13 Results ZONE CLASSIFICATION MAPS Zone Matrix Increasing Duration Increasing Depth Zone 3 (<20cm, <7days) can plant regular varieties without negative effects Zone 4 ( 20cm, <7days) Zone 2 (<20cm, 7days) Zone 1 ( 20cm, 7days) regular varieties cannot survive Depth and Duration Thresholds were derived from Mackill et. Al (2010) and Salam et. Al (2004), respectively 13

14 5-Year Zone Map 25-Year Zone Map 14

15 Percentage of cultivation zone area 5/12/ Year Zone Map Rainfall return period (years) 100 Zone 4 Zone 3 Zone 2 Zone 1 15

16 Rice varieties in Apalit Name of variety NSIC RC 216 (Tubigan 17) PSB Rc10 (Pagsanjan) NSIC Rc222 (Tubigan 18) Hybrid (SL-8H) NSIC Rc290 IR 64 NSIC Rc150 Type of variety, suitable environment Irrigated Droughttolerant, irrigated Irrigated Irrigated Salinetolerant Submergence -tolerant Irrigated Cost (Php/kg) Days to maturity Yield (cav/ha) /40 kg /40 kg / kg 4800/15 kg Resistant to flooding based on farmers experience Plant height (cm) / kg ,265/40 kg * Map Presentation and Validation The results were presented and validated on September 24, IRRI provided seedlings of submergence tolerant varieties for famers of the municipality. 16

17 Key Findings The new models are more accurate than the previously available flood maps. As rain return scenarios progress: The area of Zone 1 increases while Zone 3 decreases. Areas found in Zone 1 become priority areas for adaptation practices. Some farmers discovered that RC-150 was resistant to flooding. Instead of suggesting a non-local submergence-tolerant variety, RC-150 can be adopted by farmers in Zone 1. Conclusions Validation and calibration of flood models and maps can be conducted by consulting locals on flood experiences Local knowledge base is important when providing recommendations. Introduction of new strategies may not be necessary if there is already an existing and effective one. Zone classification maps can help pinpoint priority areas for adaptation. 17

18 Way Forward Collaboration with rice research centered institutes (IRRI, PhilRICE, etc.) Inclusion of other factors used for agriculture (soil type, temperature, etc.) Integration with other crop hazard models (drought, saline intrusion etc.) References Mackill, D., Ismail, A., Pamplona, A., Sanchez, D., Carandang, J., & Septiningsih, E. (2010). Stress Tolerant Rice varieties for Adaptation to a Changing Climate. Crop, Environment & Bioinformatics, Ram, P., Singh, B., Singh, A., Ram, P., P.N., S., Boamfa, A.,... Singh, R. K. (2002). Submergence Tolerance in rainfed rice: Physiological Basis and prospects for cultivar improvement through marker-aided breeding. Field Crop Research, Salam, M. A., Biswas, P., & Rahman, M. A. (2004). Rice research and development in the flood-prone ecosystem. Proceedings of the international workshop on plood-prone rice systems in Gazipur, Bangladesh. Los Banos, Laguna (Philippines): International Rice Research Institute 18